Many earlier studies in the MLCO literature examine the methods for end-to-end learning. For examples, popular ML algorithms such as decision tree [1]}, logistic regression [2]}, artificial neural networks [3]}, and deep reinforcement learning [4]} can be applied to learn constructive heuristics for production scheduling problems and showed that their learned heuristics outperform existing constructive heuristics in the literature. GP is also a popular learning algorithms in this category and many program representations and advanced search techniques have been proposed to improve the quality of learned heuristics [5]}, [6]}. There are several reasons for the popularity of end-to-end learning. The main reason is that computational efforts required for training in this case is low and learned model/heuristics can be used to generate good solutions very efficiently. The disadvantage of end-to-end learning is that there is no guarantee that solutions produced by these methods are optimal or near-optimal. Empirical results [1]}, [6]} showed that there is a gap in solution quality between learned constructive heuristics and optimization algorithms, which is less attractive for problems in which optimal solutions are desirable. Moreover, a meta-algorithm or a heuristic template is usually required to construct solutions, which restrict the applicability of learned heuristics. Apart from production scheduling, end-to-end learning is also applied to other CO problems such as NASA's resource-constrained scheduling problem [9]}, bin packing [10]}, graph-based problems [11]}, and routing problems [12]}, [13]}, [14]}, [15]}.
